DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, 3, 4, 6, and 7 are pending in this application.
Response to Arguments
Applicant’s arguments regarding the rejections of claims 1-7 under 35 U.S.C. 112b have been fully considered and are persuasive. The rejections have been withdrawn. However, new 35 U.S.C. 112b rejections are applied to claims 1, 3, 4, 6, and 7 based on the amendments.
Applicant's arguments regarding the 35 U.S.C. 101 rejections of claims 1-7 have been fully considered and are persuasive. The rejections have been withdrawn.
Applicant's arguments regarding the 35 U.S.C. 102 rejections of claims 1-7 have been fully considered but they are moot in light of the references being applied in the current rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 4, 6, and 7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1, 6, and 7 (line numbers refer to claim 1):
Line 6 recites “the device” and it is unclear if this refers to autonomously movable device.
Lines 17-20 recite “for each of the sensor data portions, no single one of the corresponding sub-data includes an entirety of the respective sensor data portion” but it is unclear what this means. The limitation “the sensor data portions” lack antecedent basis. Lines 9-10 recite “a respective sensor data portion for each of the plurality of object” so it is unclear what “the respective sensor data portion” refers to since there is a respective sensor data portion for each of the plurality of objects.
Lines 24-27 “transmitting…the plurality of subtasks and the corresponding sub-data to respective different external data-processing resources” and it is unclear what “the corresponding sub-data” means. Does it means corresponding sub-data for one of the sub-tasks or corresponding sub-data for each of the plurality of subtasks? Additionally, it is unclear where the plurality of subtasks and the corresponding sub-data is being transmitted to. Is the plurality of subtasks transmitted to an external data-processing resource different from the external data-processing resource that the corresponding sub-data is transmitted to?
Line 30 recites “the corresponding subtask on the corresponding sub-data” but it is unclear what this refers to.
As per claim 4:
Line 3 recites “the respective sub-data” but it is unclear what this refers to.
Claims 3 and 4 are dependent claims of claim 1, and fail to resolve the deficiencies of claim 1, so they are rejected for the same reasons.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 6, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230192121 A1 hereinafter Zhang) in view of Mock et al. (US 20140298409 A1 hereinafter Mock), in view of Liu et al. (US 10966069 B1 hereinafter Liu), and further in view of Du et al. (US 20200257310 A1 hereinafter Du).
As per claim 1, Zhang teaches a computer-implemented method performed by an autonomously movable device comprising one or more sensors, a processor, and a wireless communication interface, the method comprising the following steps ([0015] An Autonomous Vehicle (AV) is a motorized vehicle that can navigate without a human driver. An exemplary autonomous vehicle includes a plurality of sensor systems; [0022] The AV 102 can additionally include a local computing device 110 that is in communication with the sensor systems 104-108, the mechanical systems 130-138, the data center 150, and the client computing device 170, among other systems. The local computing device 110 can include one or more processors; [0044] the sensor(s) 305 include one or more positioning receiver(s), such as GNSS receiver(s), GPS receiver(s), accelerometer(s), altimeter(s), barometer(s), Wi-Fi transceivers, cellular network transceivers, wireless local area network (WLAN) transceivers):
obtaining, by the autonomously movable device and from the one or more sensors, sensor data representing an environment of the device, the sensor data comprising sensor-data elements representing a plurality of objects located in the environment ([0023] The perception stack 112 can enable the AV 102 to “see” (e.g., via cameras, LIDAR sensors, infrared sensors, etc.), “hear” (e.g., via microphones, ultrasonic sensors, RADAR, etc.), and “feel” (e.g., pressure sensors, force sensors, impact sensors, etc.) its environment using information from the sensor systems 104-108; [0065] In some examples, the trained ML model(s) 347 also receive the image data 312 and/or other sensor data from other sensor(s) 305 as input(s). In response to receiving these input(s), the trained ML model(s) 347 are trained to output map data 350, which includes the objects positioned on the map; Abstract A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment; [0001] The present technology generally pertains to clustering of points in depth information from a depth sensor. More specifically, the present technology pertains to class-aware clustering of points based on semantic segmentation of the points, for instance to classify the points by object type or class);
semantically segmenting, by the processor of the autonomously movable device, the sensor data to determine a respective sensor data portion for each of the plurality of objects, each sensor data portion including all those of the sensor-data elements that represent the respective object (Abstract A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment. The mapping system uses one or more trained machine learning models to perform semantic segmentation of the plurality of points; [0042] In some examples, a described further herein, the AV 102 may perform semantic segmentation to classify a first subset of the points as belonging to a first type of object, classify a second subset of the points as belonging to a second type of object; [0049] The semantic segmentation engine 315 identifies a type or class of object that each point in at least a subset of the point data 310 belongs to. For instance, for each point in the point data 310, the semantic segmentation engine 315 can identify whether the point represents a part of a pedestrian, a part of a bicyclist (e.g., person and/or bicycle), a part of a scooter, a part of a car, a part of a truck, a part of a motorcyclist (e.g., person and/or motorcycle), a part of a plant (e.g., a tree), a part of a structure (e.g., a building, a house, a lamp post));
determining, by the autonomously movable device, a processing task to be performed on the sensor data ([0098] At operation 715, the analysis system is configured to, and can, cluster the plurality of points into a plurality of clusters based on the semantic segmentation; [0090] The analysis system includes, for instance, the AV 102, the local computing device 110, the sensor systems 104-108; [0002] Autonomous vehicles (AVs) are vehicles having computers and control systems that perform driving and navigation tasks; [0001] The present technology generally pertains to clustering of points in depth information from a depth sensor.);
dividing, by the autonomously movable device, the processing task into a plurality of subtasks (Fig. 2; [0098] At operation 715, the analysis system is configured to, and can, cluster the plurality of points into a plurality of clusters based on the semantic segmentation. At least a portion of the first subset of the plurality of points are clustered into a first cluster. At least a portion of the second subset of the plurality of points are clustered into a second cluster. Examples of the clustering of the plurality of points into the plurality of clusters based on the semantic segmentation includes the generation (e.g., the clustering) of the clustered point data 330 based on the semantic segmentation of the categorized point data 320, the generation (e.g., the clustering) of the first cluster 510 based on the car label 575 for the points corresponding to the van 530, the generation (e.g., the clustering) of the second cluster 520 based on the pedestrian label 570 for the points corresponding to the pedestrian 535; [0042] For instance, the AV 102 can configure the clustering algorithm(s) to ensure that points classified as belonging to motor vehicles, and points belonging to pedestrians, do not get clustered together. This can help to ensure that the clustering algorithm(s) properly cluster the points of the cluster 230 into the cluster 230, and the points of the cluster 245 into the cluster 245);
for each of the subtasks, determining corresponding sub-data, wherein the determining of the corresponding sub-data is performed such that, for each of the sensor data portions, no single one of the corresponding sub-data includes an entirety of the respective sensor data portion (Figs. 2 and 5; [0098] At operation 715, the analysis system is configured to, and can, cluster the plurality of points into a plurality of clusters based on the semantic segmentation. At least a portion of the first subset of the plurality of points are clustered into a first cluster. At least a portion of the second subset of the plurality of points are clustered into a second cluster. Examples of the clustering of the plurality of points into the plurality of clusters based on the semantic segmentation includes the generation (e.g., the clustering) of the clustered point data 330 based on the semantic segmentation of the categorized point data 320, the generation (e.g., the clustering) of the first cluster 510 based on the car label 575 for the points corresponding to the van 530, the generation (e.g., the clustering) of the second cluster 520 based on the pedestrian label 570 for the points corresponding to the pedestrian 535; [0042] In some examples, a described further herein, the AV 102 may perform semantic segmentation to classify a first subset of the points as belonging to a first type of object, classify a second subset of the points as belonging to a second type of object, and so forth. The AV 102 can provide the results of the semantic segmentation to the clustering algorithm(s) and can configure the clustering algorithm(s) to ensure that points classified as belonging to different types of objects do not get clustered together. For instance, the AV 102 can configure the clustering algorithm(s) to ensure that points classified as belonging to motor vehicles, and points belonging to pedestrians, do not get clustered together. This can help to ensure that the clustering algorithm(s) properly cluster the points of the cluster 230 into the cluster 230, and the points of the cluster 245 into the cluster 245, rather than mistakenly clustering the points of clusters 230 and 245 into a single cluster. In some examples, the AV 102 can configure the clustering algorithm(s) to provide different threshold distances, or cluster radiuses, for clustering points belonging different types of objects. For example, in FIG. 2, a cluster radius 210 for motor vehicles 215 (as a type of object) is illustrated as the respective circles around clusters 230 and 235 (which represent cars), and is larger than a cluster radius 220 for pedestrians 225 (as a type of object), which is illustrated as the respective circles around clusters 240 and 245 (which represent pedestrians); For example in Figure 2, a respective sensor data portion can be the points regarding motor vehicles and a corresponding sub-data can be points regarding a specific motor vehicle such as the points in cluster 230. The points in cluster 230 do not include the entirety of points regarding motor vehicles.);
each partial-result output having been produced by executing the corresponding subtask on the corresponding sub-data (Fig. 3; [0057] In some examples, the clustering engine 325 outputs clustered point data 330. The clustered point data 330 clusters different sets of points in the point data 310 into clusters based on distance between points and/or based on the semantic segmentation by the semantic segmentation engine 315. The clustered point data 330 is illustrated in FIG. 3 as clustering the eight points of the categorized point data 320 into four clusters. The four clusters are indicated by circles within which clustered point fall inside; [0042] For example, in FIG. 2, a cluster radius 210 for motor vehicles 215 (as a type of object) is illustrated as the respective circles around clusters 230 and 235 (which represent cars), and is larger than a cluster radius 220 for pedestrians 225 (as a type of object), which is illustrated as the respective circles around clusters 240 and 245 (which represent pedestrians););
reconstructing, by the processor of the autonomously movable device, a complete processing-task result based on the plurality of partial-result outputs (Fig. 3; [0017] At least some of the first subset of the plurality of points are clustered into a first cluster and at least some of the second subset of the plurality of points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters; [0064] The mapping engine 345 combines the bounded point data 340, the clustered point data 330, and/or the categorized point data 320 with a map of the environment to identify positions and/or poses (e.g., location and/or orientation) of the objects bounded by the boundaries within the map of the environment; [0066] In some examples, the mapping engine 345 outputs the map data 350. The map data 350 includes, positioned on the map, the objects defined by the boundaries in the bounded point data 340, by the clusters in the clustered point data 330, and/or by the classifications in the categorized point data 320. The map data 350 is illustrated in FIG. 3 as including illustrations of a car, a bicyclist on a bicycle, and two pedestrians on a roadway; [0044] FIG. 3 is a block diagram illustrating an architecture of a depth data processing system 300. The depth data processing system 300 includes one or more sensors 305 of the AV 102; [0112] FIG. 8 shows an example of computing system 800, which can be for example any computing device making up the AV 102…the depth data processing system 300, the sensor(s) 305, the semantic segmentation engine 315, the clustering engine 325, the boundary engine 335, the mapping engine 345, the routing engine 355, and/or the feedback engine 370); and
controlling, by the processor of the autonomously movable device and based on the complete processing-task result, an autonomous movement operation of the autonomously movable device ([0017] At least some of the first subset of the plurality of points are clustered into a first cluster and at least some of the second subset of the plurality of points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters. In some aspects, the housing is a vehicle housing of a vehicle, the mapping system generates a route through the environment based on the map that the vehicle autonomously drives along; [0081] The points in the map data 505 corresponding to the pedestrian 535 are clustered in the second cluster 520 and include a pedestrian label 570 from the semantic segmentation (e.g., using the semantic segmentation engine 315). A planned route 515 (e.g., part of the route data 360 generated by the routing engine 355) is illustrated, and avoids both the pedestrian 535 and the van 530; [0072] The depth data processing system 300 can use the vehicle control system(s) 365 to cause the AV 102 to autonomously drive the route defined in the route data 360 and generated by the routing engine 355.).
Zhang fails to teach wherein the dividing is performed so as to enforce a security constraint that no single one of the subtasks is executed on an amount of the sensor data that is sufficient to reconstruct an entirety of a respective one of the representations of any of the plurality of objects; transmitting, via the wireless communication interface of the autonomously movable device and over a wireless communication network, the plurality of subtasks and the corresponding sub-data to respective different external data-processing resources that are external to and not within the autonomously movable device; receiving, via the wireless communication interface, a respective partial-result output from each of the external data-processing resources.
However, Mock teaches wherein the dividing is performed so as to enforce a security constraint that no single one of the subtasks is executed on an amount of the sensor data that is sufficient to reconstruct an entirety of a respective one of the representations of any of the plurality of objects ([0028] The object separator 110 receives the secure object 104 and divides it into one or more secure sub-objects 204, each including data 208 and a context 206 associated therewith, as was described above. In one embodiment wherein the secure object 104 includes a digital representation, such as a scan, of a form document having predefined fields for the entry of handwritten information, the object separator 110 may utilize a template or overlay which identifies the various regions of the document associated with the form fields allowing each region, i.e. each field, to be identified and separated out. The template or overlay may be defined using Extensible Markup Language ("XML") or other suitable methodology. Each of the secure sub-objects 204 is then provided to the object disassociator 112 which disassociates the data 208 from the context 206, such as by separating the label or tag from the data 208 which it describes, or by modifying the data 208, e.g. decomposing it into smaller portions, so as to obfuscate any attributes thereof, or combinations thereof. The disassociated context 206 may be stored (not shown) for later use, such as when the job results 212 are returned for the particular sub-task 202 performed on the associated data 208; [0029] wherein the task 106 may be to transcribe a list of two hundred handwritten social security numbers into machine intelligible data, each sub-task 202 may be to similarly transcribe a portion thereof, e.g. o/Aimee Li/ ne sub-task 202 may be to transcribe the first half of each social security number while another sub-task 202 is transcribe the remaining portion; [0045] the interface may permit the requestor to designate how the task 106 and/or secure object 104 will be divided into sub-tasks 202 and sub-objects 204, e.g. by providing a template, etc., such as to ensure the protection of the underlying secure information).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhang with the teachings of Mock to protect data (see Mock [0045] the interface may permit the requestor to designate how the task 106 and/or secure object 104 will be divided into sub-tasks 202 and sub-objects 204, e.g. by providing a template, etc., such as to ensure the protection of the underlying secure information).
Zhang and Mock fail to teach transmitting, via the wireless communication interface of the autonomously movable device and over a wireless communication network, the plurality of subtasks and the corresponding sub-data to respective different external data-processing resources that are external to and not within the autonomously movable device; receiving, via the wireless communication interface, a respective partial-result output from each of the external data-processing resources.
However, Liu teaches transmitting, via the wireless communication interface of the autonomously movable device and over a wireless communication network, the plurality of subtasks and the corresponding sub-data to respective different external data-processing resources that are external to and not within the autonomously movable device (Fig. 2; Col. 13 lines 26-41 the optimization module 250 may select an optimization algorithm that causes the service allocation computing device 110 to generate a vehicle task instruction signal 190 that instructs the vehicle 130 to partition the vehicle sensor data 172 and/or the map sensor data 174 into a plurality of partitions based on the edge server state signal 180, the resolution of the HD map, and the size/type of the vehicle sensor data 172 and the map sensor data 174. Furthermore, the optimization module 250 may select an optimization algorithm that causes the service allocation computing device 110 to generate an edge server task instruction signal 200 that causes a set of the one or more edge servers 150 to process the corresponding partition and generate the HD map based on the corresponding partition when the corresponding partition is received by the set of one or more edge servers 150; Col. 14 lines 13-16 In response to receiving the corresponding partitions from the vehicle 130, each server of the set of the identified one or more edge servers 150 generates a corresponding portion of the HD map at step 434; Col. 10 lines 3-7 the network interface hardware 226 communicatively couples the vehicle system 210 to the service allocation computing device 110, the edge server computing device 120, and/or the one or more edge servers 150 via the network 140; Col. 9 lines 55-60 the vehicle system 210 can be communicatively coupled to the network 140 via a wide area network, a local area network, a personal area network, a cellular network, a satellite network, etc. Suitable local area networks may include wired Ethernet and/or wireless technologies; Col. 4 lines 6-7 the vehicle 130 may be an autonomous vehicle).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhang and Mock with the teachings of Liu to reduce latency (see Liu Col. 1 lines 24-27 The one or more edge servers provide computation and storage capability to the proximity of demand in order to reduce a latency experienced by the vehicle system and an operator of the corresponding vehicle).
Zhang, Mock, and Liu fail to teach receiving, via the wireless communication interface, a respective partial-result output from each of the external data-processing resources.
However, Du teaches receiving, via the wireless communication interface, a respective partial-result output from each of the external data-processing resources ([0060] the edge layer 18 and/or vehicle layer 16 can allocate tasks to be performed by the cloud layer 20, which can then perform the tasks and send result(s) to the edge layer 18 and/or vehicle layer 16; [0059] The cloud layer 20 may include various combinations of servers, routers, switches, processing units (e.g., central processing units (CPUs)), circuits (e.g., application specific integrated circuits (ASICs)), data storage devices, etc. that are needed to carry out different tasks associated with vehicle scene reconstruction; [0046] the wireless communications device 30 can package the onboard vehicle sensor data for wireless transmission and send the onboard vehicle sensor data to other systems or devices, such as roadside unit (RSU) 82 of the edge layer 18 and/or remote computer or server(s) 78 of the cloud layer 20; [0056] The land network 76 and/or the wireless carrier system 70 can be used to communicatively couple the cloud layer 20 with the edge layer 18 and/or the vehicle layer 16; [0033] The system and method below enable autonomous vehicles to utilize edge and cloud computing systems so as to facilitate and/or improve AV planning).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined Zhang, Mock, and Liu with the teachings of Du to improve autonomous vehicle planning (see Du [0033] The system and method below enable autonomous vehicles to utilize edge and cloud computing systems so as to facilitate and/or improve AV planning).
As per claim 3, Zhang, Mock, Liu, and Du teach the method according to claim 1. Zhang teaches wherein the sensor data are: i) one or more images, or ii) one or more point clouds ([0020] the sensor systems 104-108 can comprise Inertial Measurement Units (IMUs), cameras (e.g., still image cameras, video cameras, etc.), light sensors (e.g., cameras, image sensors, LIDAR systems; [0045] The sensor data can include, for example, depth data (e.g., one or more point clouds, depth images, depth videos, range images).
As per claim 4, Zhang, Mock, Liu, and Du teach the method according to claim 1. Zhang teaches wherein the processing task includes processing the sensor data using matrix operations, and wherein each subtask includes processing the respective sub-data using matrix operations ([0051] the semantic segmentation engine 315 outputs a similarity matrix corresponding to classification of different points of the point data 310, such that the categorized point data 320 includes the similarity matrix; [0056] The clustering engine 325 includes and/or uses one or more trained ML models 327 of one or more ML systems to cluster different subsets of the points in the categorized point data 320 into clusters; [0065] The trained ML model(s) 347, and/or the ML system(s) that train the trained ML model(s) 347, may include, for instance, NN(s) (e.g., the NN 600 of FIG. 6), CNN(s)).
Additionally, Liu teaches wherein each subtask to be outsourced includes processing the respective sub-data (Col. 13 lines 26-41 the optimization module 250 may select an optimization algorithm that causes the service allocation computing device 110 to generate a vehicle task instruction signal 190 that instructs the vehicle 130 to partition the vehicle sensor data 172 and/or the map sensor data 174 into a plurality of partitions based on the edge server state signal 180, the resolution of the HD map, and the size/type of the vehicle sensor data 172 and the map sensor data 174. Furthermore, the optimization module 250 may select an optimization algorithm that causes the service allocation computing device 110 to generate an edge server task instruction signal 200 that causes a set of the one or more edge servers 150 to process the corresponding partition and generate the HD map based on the corresponding partition when the corresponding partition is received by the set of one or more edge servers 150).
As per claim 6, it is an autonomously movable device claim of claim 1 so it is rejected for similar reasons. Additionally, Zhang teaches a processing system that includes at least one processor ([0022] The AV 102 can additionally include a local computing device 110 that is in communication with the sensor systems 104-108, the mechanical systems 130-138, the data center 150, and the client computing device 170, among other systems. The local computing device 110 can include one or more processors).
As per claim 7, it is a non-transitory computer-readable medium claim of claim 1 so it is rejected for similar reasons. Additionally, Zhang teaches a non-transitory computer-readable medium on which are stored instructions that are executable by a processor of an autonomously movable device, the instructions, when executed by the processor, causing the processor to perform the following steps (Fig. 8; [0117] Storage device 830 can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer; [0118] The storage device 830 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 810, it causes the system to perform a function; [0112] FIG. 8 shows an example of computing system 800, which can be for example any computing device making up the AV 102).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HSING CHUN LIN whose telephone number is (571)272-8522. The examiner can normally be reached Mon - Fri 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aimee Li can be reached at (571) 272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.L./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
Prosecution Insights